Spring viremia of carp virus (SVCV), which causes an acute hemorrhagic and highly contagious disease in cyprinids, was first described in Europe and subsequently reported in parts of Asia and North America. SVCV can be classified into four genogroups: Ia, Ib, Ic, and Id. While Ia and Id have wide circulation and are reported to cause outbreaks in North America and Europe, respectively, Ib and Ic were last reported in the 1980s. We used a Bayesian framework to determine the nucleotide substitution rates, relative genetic diversity, and time to the most recent common ancestor (TMRCA) of SVCV using large sets of sequences of the phosphoprotein and glycoprotein (G) genes. The sampled genetic diversities of Ia and Id were found to have arisen during the year 1996 (95% Highest Posterior Density: 1986-1998) and 1957 (1926-1972), respectively, with consistent results across the two genes. The TMRCA for SVCV was estimated to have been around 1850 (1727-1938). The substitution rate for Ia is at least 5-7 times higher than that of Id. The rate of nonsynonymous (dn) to synonymous (ds) substitutions (dn/ds=ω) for the G gene of Ia (ω=0.608) is significantly higher than that of Id (ω=0.0749), indicating both exhibit distinct selection profiles. The SVCV population experienced a bottleneck during the early 1990 s followed by a sudden rebound, primarily due to the sudden increase in genetic variants in Ia and Id, which coincided with the timing of a recent series of outbreaks reported in Europe and North America.
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http://dx.doi.org/10.1016/j.vetmic.2011.10.005 | DOI Listing |
Health Res Policy Syst
January 2025
Congdon School of Supply Chain, Business Analytics, and Information Systems, University of North Carolina Wilmington, Wilmington, NC, 28403, United States of America.
Background: The coronavirus disease 2019 (COVID-19) pandemic placed a heavy strain on the United States healthcare system. Common hospital operational performances were impacted to varying degrees by the pandemic. This study examined the healthcare operational measures during COVID-19 pandemic.
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January 2025
School of Medicine, Nankai University, Tianjin, China.
Although the triglyceride-glucose (TyG) index has been established as a valuable predictor for cardiovascular disease (CVD) and cardiovascular mortality, there is limited research exploring its association with all-cause or CVD mortality specifically in adults with diabetes aged < 65 years without cardiovascular disease. This study aimed to investigate the relationship between the TyG index and both all-cause and CVD mortality in this population within the United States. Our study recruited 1778 adults with diabetes aged < 65 years without cardiovascular disease from the National Health and Nutrition Examination Survey (NHANES) 2003-2018.
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January 2025
Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
To investigate the incidence rate, risk factors, and clinical implications of postoperative pulmonary complications (PPCs) in patients undergoing colorectal cancer surgery (CRC). The study extracted data from the National Inpatient Sample (NIS) between 2010 and 2019. Patients' data were analyzed to identify predictors of PPCs, and the association between possible factors and PPCs were also assessed.
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January 2025
The Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA.
Racial/ethnic differences are associated with the symptoms and conditions of post-acute sequelae SARS-CoV-2 infection (PASC) in adults. These differences may exist among children and warrant further exploration. We conducted a retrospective cohort study with difference-in-differences analyzes to assess these differences in children and adolescents under the age of 21.
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January 2025
Translational Dementia Research Group, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Sydney, NSW, 2145, Australia.
Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was sourced from the Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI).
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